The idea of the artificial bee colony (ABC) algorithm, as developed by Karaboga [1]}, is more suitable for continuous and discrete optimization problems. ABC algorithm is a powerful optimization algorithm, which simulates the foraging behavior of honeybees to find the best food source. ABC works based on three types of bees: 1) employed bees, 2) onlooker bees and 3) one scout bee. ABC starts with employed bees computing the goodness of each solution, then onlooker bees seek the global maximum solution with respect to the best goodness using cross over, which generates new offspring out of selected high solutions to create a better solution. This process repeats itself until no higher maximum goodness is found or the number of crossover iterations meet the pre-designed limit. After that, the scout bee goes beyond of the boundary of onlookers to seek global maximum goodness (solution). This entire process repeats until the condition is met or again it reaches the maximum number of iterations. Onlooker bees run until the limit condition is satisfied. Next, one of employed bees converts into a scout. Note that, in each iteration, Scout performs mutation, which is the final phase of evolutionary algorithms.
